D vs 9D" dataset (50,956 games).
>
> https://github.com/yenw/computer-go-dataset <https://github.com/yenw>
>
>
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since the Nature version.
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Yes, it is AlphaGo. I am relieved that DeepMind clarified this.
Honestly I got a little frustrated that many people didn't think that
was AlphaGo. It was almost clear to me because I know the difficulty of
developing AlphaGo-like bots.
I hope Aja can comment here, also about GodMoves :)
Yamato
name AlphaGo. So probably Aja cannot answer this, because
he does not lie.
By the way, I found this tweet interesting :)
https://twitter.com/ScienceNews/status/814559161312808965
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Congratulations Aja.
Do you have a plan to run AlphaGo on KGS?
It must be a 9d!
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to desktop machines with thousands of cores
in a few years...
What is your 23 core hardware? How much is it?
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for high-dan level players. 7.0 komi would give better results.
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xiefan wrote:
could anyone help give me a link in English to buy the commercial version
of Zen? Now I could only find some websites in Japanese, however, I could
not read Japanese
The commercial version of Zen is not yet available for overseas people.
Thanks for your patience.
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complex schemes. But I
don't think I implement Rave like others do either.
Your implementation must be very different from mine. Actually I don't
use Progressive widening (or unpruning) at all. It's a mystery to me why
others say it does work.
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= Zenith)
It will be available on the Internet in the near future.
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is equivalent to ZenLv6, weak KGS 1d.
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RAVE is a very efficient algorithm, it strongly limits scalability
of the program. It typically makes a fatal mistake in the position that
the order of moves are important. We definitely need to improve RAVE,
but it is a very tough job.
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criticality seems a nice idea, but how should it be implemented?
Just giving high priority to the liberties does not work, because that
cannot be distinguished from the simple dame-filling.
Can you suggest a concrete formula?
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is not
a solution for the handicap problem. Did anyone succeed with it?
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seconds per move.
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Isaac Deutsch wrote:
Did you think the bot would not benefit much from additional time usage?
I'm not sure. The fast time setting was the wish of the sponsor. The games
were played on the real board and they didn't want to take a much time.
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.
Have fun.
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Ingo Althöfer wrote:
Yamato San wrote:
The match of O Meien 9p vs Zen will be held on next Monday via KGS.
Schedule (JST)
Date: August 10
Time: 14:10
Handicap
9x9: Zen is black, with 0.5 or 3.5 komi (?)
19x19: Zen is black, with 7 handicap stones.
Thanks for the information.
* How
they don't exist too.
I have done some experiments. Zen won 30/100 games against a mechanical
mirror-go program without any anti-mirror code. The 2 continuous losses
were not unlikely, but I think there is no proof that Zen was far weaker
against mirror-go than other programs.
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you waiting. Unfortunately the project is still in the
planning stage. I cannot say when or how much it will be.
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Ingo Althöfer wrote:
Congratulations to Zen author also from me!
Thanks for your all wishes.
As far as I know, Yamato is only a nickname and not the true name
of Zen's author. If this is the case, when will the identity of
Zen's author be revealed? Did (or do) the organizers of the Pamplona
was. I am not sure how it works, but anyway I can
see your algorithm now. Thanks.
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value of the pattern, weighted by the probability of selecting
each action).
I still don't understand this. Is it the formula 6?
Could you please give me an example like the above?
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give us the source code which you used? Your algorithm is
too complicated, so it would be very helpful if possible.
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with no
tree on small boards.
I like you idea, but why do you use only 5x5 and 6x6 Go? I don't think
the 200+ Elo improvement is so impressive because the previous approaches
were not optimized for such a small boards. I'm looking forward to your
results on larger boards.
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Ingo Althöfer wrote:
Other question: Might it be possible to find a
volunteer for operating Zen19 in Pamplona?
I forgot to say it here - Mr. Kato came forward as an operator of Zen.
It will play via KGS. Have a good game, all.
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mine. If you don't
use minorization-maximization yet, I recommend studying it.
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have no specific
plans. I don't have plans about a massively parallel version either.
Unfortunately I cannot participate in the Olympiad. For KGS, I can.
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of the KGS rule.
I think I have fixed the problem now.
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Rémi Coulom wrote:
Martin Mueller wrote:
Zen has been getting very impressive results on CGOS. Yamato-san,
could you tell us a little bit about yourself and your program?
Thanks
Martin
Yes, very strong results. Congratulations to Yamato-san for beating
Crazy Stone.
I am curious
is pass, otherwise he loses. That's not
entirely correct. White loses no matter what he does, whether it's a
pass or anything else.
I meant that for when Black plays wrong move.
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. This position is win for black in Japanese
rules, but the only correct move is pass. If black plays anywhere other
than pass, he loses. This time white's correct move is pass, otherwise
he loses. Such a condition breaks winning rate values in the tree.
How should I handle this?
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jpn.sgf
I ran 86 19x19 games with both on the same hardware (single core of
A64 X2 6000+, 2G RAM) with 20 minutes S.D. each, the rate is MoGo win
83.3% (+-4.1).
How did you set the time to 20 minutes S.D.? MoGo doesn't update the
clock if you don't send time_left, and Fuego does.
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)
E3: 918/ 1742 (52% win)
C6: 863/ 1707 (50% win)
C5: 779/ 1418 (54% win)
It seems the best move is D6 or D7.
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Magnus Persson wrote:
The program reivax on 9x9 CGOS seems to be strong but suffer from a
bug leading it to pass too early, and thus it often loses games
against weaker programs that do not resign.
Who is the author of Reivax? It's like a CGOS deflator.
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David Fotland wrote:
I didnt see this:
148: D1 also wins?
You are right. Thanks for correction.
Many Faces played D1, so change it to 38 correct.
Did you use the fixed version?
I corrected #148 as follows. Is it still wrong?
loadsgf sgf/mc148.sgf
148 reg_genmove black
#? [B1|D1]
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Go could run on the MC only mode, it might be more
interesting, I guess.
Gunnar Farnebäck wrote:
One more correction. The fixed version added B1 as a correct move in
113, but that point is occupied.
My mistake. There will be no effect on the results, anyway.
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= win).
Do you mean you found some problems are wrong?
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.
Yes, but will not win with komi 7.5.
143: I don't see how A3 could win the semeai. A2 and C4 look more
effective.
Typo, it was A2. C4 cannot work.
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. In a way this is somewhat circular: we use the
best estimate so far to compute the best new estimate. But I don't
think that is unreasonable in this case.
Thanks for the detailed explanation. The formula became clear to me.
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, can anyone write the pseudo-code of this algorithm?
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) / 20
I think it is a little better than the one on the ICML paper.
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it is CGOS deflation.
The evidence is:
ControlBoy 1546 8/8 100.00 - Zen-0.9
ControlBoy 1460 5/5 100.00 - Zen-1.0
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or more playouts
per move are needed to judge whether a change is good or bad.
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the play-outs.
I fully agree.
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-Terry Model. greenpeep uses similar patterns extracted from
the offline self-play.
Then I have tested a lot of change of probability distributions, but
it was very hard to improve the strength.
Any comments?
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for captures/saves and local moves):
84%
Thanks for interesting results.
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with MoGo-style one? (the winning rate against GNU Go, etc)
Also, how large and how many are your patterns?
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time to find it.
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Yes you can:
--nbTotalSimulations 3000
Once you set this option it ignores all other time settings.
Thanks! It works perfectly.
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Have you tried Visual C++?
http://msdn2.microsoft.com/en-us/express/aa975050.aspx
The thing is that VC++ does not have the pthread library.
This library might be help.
http://sources.redhat.com/pthreads-win32/
# I have not used it, though
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as possible.
Have you tried Visual C++?
http://msdn2.microsoft.com/en-us/express/aa975050.aspx
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MonteGNU is published, it will be an alternative of GNU Go.
Of course MC vs MC may have some problems, but at least it is
stronger than GNU Go on 9x9.
And, if the way to combine UCT and the local tactical search is
discovered, the regression test like GNU Go will be also useful.
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correctly, so
pruning such moves decrease the winning rate.
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moves in the playouts do not always improve
the performance of UCT. What do you think about this claim?
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is this inappropriate notation still used?
I agree that the pseudo-code is easy to understand.
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for the translation. The only point I am still missing: What is
RAVE(AMAF)?
RAVE is another name of AMAF(all moves as first) heuristic.
The details of AMAF are explained in this paper.
http://www.ai.univ-paris8.fr/~bh/articles/acg10-mcgo.pdf
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round?
All experiments (except the default policy) were played against GnuGo
level 10, not level 8.
That's surprising news to me...
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it does not change
really the level of gnugo.
Because I have tested my program against GNU Go level 8 to compare the
winning rate with MoGo. Now I see surely there is almost no change.
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another question; Don't you use Q_RLGO anymore?
If so, would you explain the detail of the Q_MoGo heuristic?
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for that. These values were more extreme than my expectation.
I thought you use values like 0.4 or 0.6.
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been run. How did you determine these numbers?
Thanks
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inform me!
If you want, I recommend Download.com.
But I would rather you put it on CGOS or KGS.
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